How to use the pmdarima.utils.array.c function in pmdarima

n = x.shape[0]

        # check on status of null
        null = self.null

        # fit a model on an arange to determine the residuals
        if null == 'trend':
            t = np.arange(n).reshape(n, 1)

            # these numbers came out of the R code.. I've found 0 doc for these
            table = c(0.216, 0.176, 0.146, 0.119)
        elif null == 'level':
            t = np.ones(n).reshape(n, 1)

            # these numbers came out of the R code.. I've found 0 doc for these
            table = c(0.739, 0.574, 0.463, 0.347)
        else:
            raise ValueError("null must be one of %r" % self._valid)

        # fit the model
        lm = LinearRegression().fit(t, x)
        e = x - lm.predict(t)  # residuals

        s = np.cumsum(e)
        eta = (s * s).sum() / (n**2)
        s2 = (e * e).sum() / n

        # scalar, denom = 10, 14
        # if self.lshort:
        #     scalar, denom = 3, 13
        # l_ = int(np.trunc(scalar * np.sqrt(n) / denom))
        if self.lshort:

Notes
    -----
    This test is generally not used directly, but in conjunction with
    :func:`pmdarima.arima.nsdiffs`, which directly estimates the number
    of seasonal differences.

    References
    ----------
    .. [1] Testing for seasonal stability using the Canova
           and Hansen test statisic: http://bit.ly/2wKkrZo

    .. [2] R source code for CH test:
           https://github.com/robjhyndman/forecast/blob/master/R/arima.R#L148
    """
    crit_vals = c(0.4617146, 0.7479655, 1.0007818,
                  1.2375350, 1.4625240, 1.6920200,
                  1.9043096, 2.1169602, 2.3268562,
                  2.5406922, 2.7391007)

    def __init__(self, m):
        super(CHTest, self).__init__(m=m)

    @staticmethod
    def _sd_test(wts, s):
        # assume no NaN values since called internally
        # also assume s > 1 since called internally
        n = wts.shape[0]

        # no use checking, because this is an internal method
        # if n <= s:  raise ValueError('too few samples (%i<=%i)' % (n, s))
        frec = np.ones(int((s + 1) / 2), dtype=np.int)

if not self._base_case(x):
            return np.nan, False

        # ensure vector
        x = check_endog(x, dtype=DTYPE)
        n = x.shape[0]

        # check on status of null
        null = self.null

        # fit a model on an arange to determine the residuals
        if null == 'trend':
            t = np.arange(n).reshape(n, 1)

            # these numbers came out of the R code.. I've found 0 doc for these
            table = c(0.216, 0.176, 0.146, 0.119)
        elif null == 'level':
            t = np.ones(n).reshape(n, 1)

            # these numbers came out of the R code.. I've found 0 doc for these
            table = c(0.739, 0.574, 0.463, 0.347)
        else:
            raise ValueError("null must be one of %r" % self._valid)

        # fit the model
        lm = LinearRegression().fit(t, x)
        e = x - lm.predict(t)  # residuals

        s = np.cumsum(e)
        eta = (s * s).sum() / (n**2)
        s2 = (e * e).sum() / n

References
    ----------
    .. [1] https://wikipedia.org/wiki/Augmented_Dickey–Fuller_test
    .. [2] R's tseries ADF source code: https://bit.ly/2EnvM5V
    """
    table = np.array([
        (-4.38, -3.95, -3.60, -3.24, -1.14, -0.80, -0.50, -0.15),
        (-4.15, -3.80, -3.50, -3.18, -1.19, -0.87, -0.58, -0.24),
        (-4.04, -3.73, -3.45, -3.15, -1.22, -0.90, -0.62, -0.28),
        (-3.99, -3.69, -3.43, -3.13, -1.23, -0.92, -0.64, -0.31),
        (-3.98, -3.68, -3.42, -3.13, -1.24, -0.93, -0.65, -0.32),
        (-3.96, -3.66, -3.41, -3.12, -1.25, -0.94, -0.66, -0.33)
    ])

    tablen = table.shape[1]
    tableT = c(25, 50, 100, 250, 500, 100000)
    tablep = c(0.01, 0.025, 0.05, 0.10, 0.90, 0.95, 0.975, 0.99)

    def __init__(self, alpha=0.05, k=None):
        super(ADFTest, self).__init__(alpha=alpha)

        self.k = k
        if k is not None and k < 0:
            raise ValueError('k must be a positive integer (>= 0)')

    @staticmethod
    def _ols(x, y, z, k):
        n = y.shape[0]
        yt = z[:, 0]  # type: np.ndarray
        tt = np.arange(k - 1, n)

        # R does [k:n].. but that's 1-based indexing and inclusive on the tail

yleft : float, optional (default=None)
        The value to be returned when input ``x`` values are less than
        ``min(x)``. The default is defined by the value of rule given below.

    yright : float, optional (default=None)
        The value to be returned when input ``x`` values are greater than
        ``max(x)``. The default is defined by the value of rule given below.

    ties : str, optional (default='mean')
        Handling of tied ``x`` values. Choices are "mean" or "ordered".
    """
    if method not in VALID_APPROX:
        raise ValueError('method must be one of %r' % VALID_APPROX)

    # make sure xout is an array
    xout = c(xout).astype(np.float64)  # ensure double

    # check method
    method_key = method

    # not a callable, actually, but serves the purpose..
    method = get_callable(method_key, VALID_APPROX)

    # copy/regularize vectors
    x, y = _regularize(x, y, ties)
    nx = x.shape[0]

    # if len 1? (we've already handled where the size is 0, since we check that
    # in the _regularize function when we call c1d)
    if nx == 1:
        if method_key == 'linear':
            raise ValueError('need at least two points to '

lshort : bool, optional (default=True)
        Whether or not to truncate the ``l`` value in the C code.

    Notes
    -----
    This test is generally used indirectly via the
    :func:`pmdarima.arima.ndiffs` function, which computes the
    differencing term, ``d``.

    References
    ----------
    .. [1] R's tseries KPSS test source code: http://bit.ly/2eJP1IU
    """
    _valid = {'trend', 'null'}
    tablep = c(0.01, 0.025, 0.05, 0.10)

    def __init__(self, alpha=0.05, null='level', lshort=True):
        super(KPSSTest, self).__init__(alpha=alpha)

        self.null = null
        self.lshort = lshort

    def should_diff(self, x):
        """Test whether the time series is stationary or needs differencing.

        Parameters
        ----------
        x : array-like, shape=(n_samples,)
            The time series vector.

        Returns

.. [1] R's tseries PP test source code: http://bit.ly/2wbzx6V
    """
    table = -np.array([
        (22.5, 25.7, 27.4, 28.4, 28.9, 29.5),
        (19.9, 22.4, 23.6, 24.4, 24.8, 25.1),
        (17.9, 19.8, 20.7, 21.3, 21.5, 21.8),
        (15.6, 16.8, 17.5, 18.0, 18.1, 18.3),
        (3.66, 3.71, 3.74, 3.75, 3.76, 3.77),
        (2.51, 2.60, 2.62, 2.64, 2.65, 2.66),
        (1.53, 1.66, 1.73, 1.78, 1.78, 1.79),
        (0.43, 0.65, 0.75, 0.82, 0.84, 0.87)
    ]).T

    tablen = table.shape[1]
    tableT = c(25, 50, 100, 250, 500, 100000).astype(DTYPE)
    tablep = c(0.01, 0.025, 0.05, 0.10, 0.90, 0.95, 0.975, 0.99)

    def __init__(self, alpha=0.05, lshort=True):
        super(PPTest, self).__init__(alpha=alpha)

        self.lshort = lshort

    def should_diff(self, x):
        """Test whether the time series is stationary or needs differencing.

        Parameters
        ----------
        x : array-like, shape=(n_samples,)
            The time series vector.

        Returns
        -------

----------
    .. [1] R's tseries PP test source code: http://bit.ly/2wbzx6V
    """
    table = -np.array([
        (22.5, 25.7, 27.4, 28.4, 28.9, 29.5),
        (19.9, 22.4, 23.6, 24.4, 24.8, 25.1),
        (17.9, 19.8, 20.7, 21.3, 21.5, 21.8),
        (15.6, 16.8, 17.5, 18.0, 18.1, 18.3),
        (3.66, 3.71, 3.74, 3.75, 3.76, 3.77),
        (2.51, 2.60, 2.62, 2.64, 2.65, 2.66),
        (1.53, 1.66, 1.73, 1.78, 1.78, 1.79),
        (0.43, 0.65, 0.75, 0.82, 0.84, 0.87)
    ]).T

    tablen = table.shape[1]
    tableT = c(25, 50, 100, 250, 500, 100000).astype(DTYPE)
    tablep = c(0.01, 0.025, 0.05, 0.10, 0.90, 0.95, 0.975, 0.99)

    def __init__(self, alpha=0.05, lshort=True):
        super(PPTest, self).__init__(alpha=alpha)

        self.lshort = lshort

    def should_diff(self, x):
        """Test whether the time series is stationary or needs differencing.

        Parameters
        ----------
        x : array-like, shape=(n_samples,)
            The time series vector.

        Returns